来自希腊雅典农业大学的科学家，利用Videometer多光谱成像系统发表了题为“Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses”的文章，文章发表于知名期刊Sensors 2022, 22(18), 7018; https://doi.org/10.3390/s22187018。

利用基于光谱学、成像分析和模拟人类感官的传感器技术快速评估食用海藻的微生物质量

摘要

海藻养殖业的扩张以及这些产品的快速腐败，使得实施快速、实时的质量评估技术变得更加重要。原产于苏格兰和爱尔兰的海藻样本在不同温度条件下储存特定时间间隔。在整个储存过程中进行微生物分析以评估总活菌数（TVC），同时进行平行的FT-IR光谱、多光谱成像（MSI）和电子鼻（e-nose）分析。机器学习模型（偏最小二乘回归（PLS-R））用于评估传感器和微生物数据之间的任何相关性。微生物计数在1.8至9.5 log CFU/g之间，而微生物生长速度受产地、收获年份和储存温度的影响。使用FT-IR数据开发的模型在外部测试数据集上显示出良好的预测性能。通过合并来自两个来源的数据开发的模型产生了令人满意的预测性能，显示出更强的稳健性，因为对微生物种群预测不了解来源。使用MSI数据开发的模型结果表明，尽管RMSE值较高，但在外部测试数据集上的预测性能相对较好，而在使用MI和SAMS的电子鼻数据时，报告的模型预测性能较差。

关键词：海藻；腐败；红外光谱；多光谱成像；电子鼻；机器学习 

Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses

Sensors 2022, 22(18), 7018; https://doi.org/10.3390/s22187018

Abstract

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.View Full-Text

Keywords: marine algae; spoilage; FT-IR; multispectral imaging; e-nose; machine learning